{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### An example showing the plot_pca_component_variance method used by a scikit-learn PCA object\n",
    "\n",
    "In this example we'll use scikit-plot's `sklearn.decomposition.plot_pca_component_variance` on our PCA algorithm to see the variance in our feature embeddings. First, we'll create an instance of the PCA algorithm, then fit our model with the data, and pass this fitten instance into our scikit-plot method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "from sklearn.decomposition import PCA\n",
    "from sklearn.datasets import load_digits as load_data\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Import scikit-plot\n",
    "import scikitplot as skplt\n",
    "\n",
    "%pylab inline\n",
    "pylab.rcParams['figure.figsize'] = (14, 14)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PCA(copy=True, iterated_power='auto', n_components=None, random_state=1,\n",
       "  svd_solver='auto', tol=0.0, whiten=False)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load the dataset\n",
    "X, y = load_data(return_X_y=True)\n",
    "\n",
    "# Create PCA instance then fit\n",
    "pca = PCA(random_state=1)\n",
    "pca.fit(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GQW2IDgvE4Prd796E2V/tYsmmo/j7GQoKLZfHNeXOhI7uemBwPWbAGHh+2Q4+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x272ef794cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot!\n",
    "skplt.decomposition.plot_pca_component_variance(pca)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
